Abstract

Digital 3D models from the domain of architecture - e.g. models describing buildings, furniture or building blocks - have replaced analogue paper-based drawings as well as haptic scale models bit by bit during the last five decades. Specialized libraries with an obligation to collect and archive important documents from this domain therefore face new challenges that come along with the digital 3D representation. One of the main obstacles for easy integration of 3D models in digital libraries and archives is posed by the fact that authors mostly provide only sparse and very heterogeneous metadata annotations. Ensuring high-quality cataloging would therefore require a huge amount of tedious and expensive manual work by experts from the architectural domain, especially when regarding the ever increasing amount of important digital 3D data. To overcome these problems, we propose an automated multi-label categorization that relies on techniques from machine learning. In contrast to text categorization approaches, the algorithm is based on geometric properties instead of letters and words. To keep the required human working effort and the according costs as low as possible we use publicly available 3D model tags for learning instead of letting experts generate annotations.

Bilder

Bibtex

@INPROCEEDINGS{bluemel-2013-towards,
author = {Bl{\"u}mel, Ina and Wessel, Raoul and Klein, Reinhard},
title = {Towards an automatic multi-label classification of 3D architectural models},
booktitle = {Workshop on Classification and Subject Indexing in Library and Information Science (LIS 2013)},
year = {2013},
month = jul,
location = {Neum{\"u}nster Abbey Cultural Exchange Center (CCRN), Luxembourg},
howpublished = {Accepted for oral presentation},
keywords = {3D-MODELS, DIGITAL LIBRARIES, LIBRARY CATALOGING, METADATA},
abstract = {Digital 3D models from the domain of architecture - e.g. models describing buildings, furniture or
building blocks - have replaced analogue paper-based drawings as well as haptic scale models bit by
bit during the last five decades. Specialized libraries with an obligation to collect and archive
important documents from this domain therefore face new challenges that come along with the digital
3D representation. One of the main obstacles for easy integration of 3D models in digital libraries
and archives is posed by the fact that authors mostly provide only sparse and very heterogeneous
metadata annotations. Ensuring high-quality cataloging would therefore require a huge amount of
tedious and expensive manual work by experts from the architectural domain, especially when
regarding the ever increasing amount of important digital 3D data. To overcome these problems, we
propose an automated multi-label categorization that relies on techniques from machine learning. In
contrast to text categorization approaches, the algorithm is based on geometric properties instead
of letters and words. To keep the required human working effort and the according costs as low as
possible we use publicly available 3D model tags for learning instead of letting experts generate
annotations.}
}